units

ETW3482

Faculty of Business and Economics

Undergraduate - Unit

This unit entry is for students who completed this unit in 2015 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

print version

6 points, SCA Band 3, 0.125 EFTSL

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.

LevelUndergraduate
FacultyFaculty of Business and Economics
Organisational UnitDepartment of Econometrics and Business Statistics
OfferedNot offered in 2015
Coordinator(s)Associate Professor Santha Vaithilingam

Synopsis

This unit aims to provide an understanding and application of the tools and techniques of data mining in delivering superior value added propositions to businesses. Students will learn the data mining methodology, appropriate techniques to apply in different cases, practical use of data mining software and how to interpret the knowledge generated from these tools. Students will be exposed to emerging areas in data mining, such as applications of data mining in the cloud.

Students will also learn about ethical concerns on the use of data mining. Superior data mining skills and knowledge enables the business to maximise the value of current customers, through creative and critical analysis of favourable circumstances and possibilities for gaining increasing business and or reducing costs from current customers.

Outcomes

The learning goals associated with this unit are to:

  1. appreciate the different stages of the data mining process
  2. understand the role that data mining play in various areas of business
  3. select the appropriate data mining tools to suit the problem at hand
  4. have hands-on experience on using data mining tools on various case studies such as direct marketing campaigns and product introductions, and analysing customer churn
  5. interpret and apply critical thinking on the results generated from the data mining models.

Assessment

Within semester assessment: 50%
Examination: 50%

Workload requirements

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning, peer directed learning and online engagement.

See also Unit timetable information

Chief examiner(s)

Prerequisites

ETW1000 or ETW1102 or ETC1000 or equivalent.

Prohibitions

FIT3002, CSE3212, GCO3828 or equivalent.